Segmenting non stationary images with triplet Markov fields

The hidden Markov field (HMF) model has been used in many model-based solutions to image analysis problems, including that of image segmentation, and generally gives satisfying results. However, when the class image is non stationary, the unsupervised segmentation results provided by HMF can be poor. In this paper, we tackle the problem of modeling a non stationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, to segment non stationary images. Experiments indicate that the new algorithm performs better than the classical one.